Predictive Analytics & Rising Product Recalls: Two Key Watchwords For Manufacturers In 2025
In 2024, for the second consecutive year, there were more than 3,200 product recall events recorded in the U.S. alone according to Sedgwick’s “2025 U.S. State of the Nation Recall Index” report. This represents the second-highest annual total in the past six years, and it is expected that product recalls will continue to dominate the headlines and frustrations of manufacturers around the world throughout 2025.
The persistent product recall situation may well be what has shaped another key prediction ETQ shared for 2025: The growing reliance on predictive AI-driven analytics, which leverages enterprise-wide data to proactively address the problems in manufacturing that cause faulty parts, products and processes and lead to product recalls in the first place.
Surge in Product Recalls
In recent years, product recalls have become an increasingly common occurrence across various industries, ranging from food and beverage to aircraft and automotive manufacturing. As the number of recalls rises, the impact on manufacturers has grown, leading to significant financial costs, legal fees and other repercussions and a hit to brand reputation.
Several factors contribute to the rising number of product recalls. They are largely tied to the complexity of today’s products and the lengthy supply chain, rising regulatory pressure and the power of the user in an age of constant feedback and social media.
Interconnected systems, software components, and more intricate mechanical parts, raise the likelihood of a defect or failure increases,pecially in the manufacture of electronics/appliances, vehicles and similar manufacturing sectors. A malfunction in any single component can have cascading effects, leading to widespread recalls. The complexity and long tail of the supply chain also makes it difficult to oversee and forge deep supplier relationships—or even to properly understand who exactly is manufacturing your parts or supplies and where.
Stricter and broader regulatory standards also are forcing products that don’t meet those standards to be recalled. For instance, agencies such as the U.S. Consumer Product Safety Commission (CPSC) or the U.K. Medicines and Healthcare Products Regulatory Agency (MHRA), are quick to issue recalls when they identify unsafe conditions that can put public health at risk.
Another contributing factor is the growing importance of customer feedback and users’ ability to share their experiences or safety concerns immediately on social media, which can quickly go viral. This has placed greater pressure on manufacturers to take decisive action and recall products before any harm is done.
Predictive Analytics – An Antidote to Product Recalls
There will be no silver bullet to resolving the product recall epidemic in 2025, but manufacturers will get smarter about how they work to prevent it. Predictive quality analytics driven by AI will identify problems before they have a chance to wreak havoc. Predictive analytics uses data, machine learning and statistical models to forecast future outcomes. It allows manufacturers to use both historical and current data to forecast issues and failures with increasing accuracy.
Some of the ways manufacturers will use predictive analytics include the following:
Predictive Maintenance: Many times, product failures are a direct result of faulty equipment. To optimize the performance and lifespan of plant equipment, predictive analytics can continually assess equipment health in real time, often by collecting data from sensors to identify, detect and address issues as they occur, as well as predict the potential future state of equipment.
Automated Alerts: Predictive analytics-based systems can send early warning triggers when a process or machine is at risk of producing defects. This early warning system can mean the difference between full production output and a costly product recall.
Root-Cause Analysis: When problems are detected in advance thanks to predictive analytics, the enterprise-wide data that was collected can be used to automate root-cause analysis, as well as inform corrective actions that should be taken to rectify them.
With ever-increasing amounts of data from complex manufacturing equipment and processes, predictive analytics is helping manufacturers take a proactive approach to quality management, preventing problems before they occur, ensuring product consistency, faster decision making and ultimately better quality.
In the first quarter of 2025, we’ve already experienced an inordinate number of recalls that have us questioning the safety of the snacks we consume, the cars we drive and the safety seats we use for our children. Yet, thanks to AI-driven predictive analytics that give manufacturers data-driven insights, technology will continue to lead the way throughout the year, helping humans build better and safer products.
Author: David Isaacson Vice President of Product Marketing at ETQ, where he develops market strategies and product positioning for the company’s cloud-based quality management solutions.
For more information: www.etq.com
